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Temporal probability analysis of landslides triggered by intense rainfall in the Bamenda Mountain Region, Cameroon

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Abstract

The monsoon season in Cameroon is often marked by heavy rainfall and landslides across Bamenda Mountain Region causing heavy material damage and human casualties. This paper presents a model to assess the temporal probability of rainfall-induced landslides in the area. Landslide events from 1994 to 2013 collected from literature review, field work, aerial photo and satellite image interpretation, and rainfall data from the Bamenda weather station for the same period were used to determine rainfall thresholds for landslide initiation. Two threshold determination methods (intensity–duration and daily antecedent rainfall) were used to determine rainfall characteristics that initiate landslides. Then the number of times rainfall exceeded the different thresholds was estimated, and the results were fitted into the poison model to assess the temporal probability of landsliding for 1, 5, 10 and 25-year return period. Each of the thresholds was validated using rainfall and landslide events for 2009 that were not used in building the model. The results showed that rainfall intensity of 94 mm in 1 day and rainfall of 96 mm with 0.49 of 10-day antecedent rainfall triggered landslides in the area. The validation of these results showed only one landslide falling below each of the threshold curves. The temporal probability of landslides varied from 0.27 to 0.38 using the rainfall intensity–duration approach and 0.28–0.41 for the daily antecedent rainfall approach. The true forecast rate stood at 89 %, and the failure to predict probability was 0.11. The model shows a high correlation between rainfall and landsliding, and the temporal probability can be used for land-use planning and civil protection.

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Correspondence to Roland Ngwatung Afungang.

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Afungang, R.N., Bateira, C.V. Temporal probability analysis of landslides triggered by intense rainfall in the Bamenda Mountain Region, Cameroon. Environ Earth Sci 75, 1032 (2016). https://doi.org/10.1007/s12665-016-5835-7

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